Abstract:Although numerical models of heat transfer and material flow have contributed to understand the underlying mechanisms of friction stir welding (FSW), there are certain input model parameters that can not be easily determined. Thus, the model predictions do not always agree with experimental results. In this work, sensitivity analysis and parameter estimation were applied to test heat transfer and material flow models. A forward-difference approximation was used to compute the sensitivity of the solution with r… Show more
“…They concluded that at high noise levels, Parallel Tempering (PT) and Evolutionary Monte Carlo (EMC) perform equally and estimate the parameter with the deviation of maximum 9%. Sensitivity analysis [15][16][17][18][19] is an important study that indicates the effectiveness of the change in parameter estimated on the temperature. Sensitivity analysis has control over the selection of the inverse method.…”
This paper reports the estimation of the unknown boundary heat flux from a fin using the Bayesian inference method. The setup consists of a rectangular mild steel fin of dimensions 250915096 mm 3 and an aluminium base plate of dimensions 250915098 mm 3. The fin is subjected to constant heat flux at the base and the fin setup is modelled using ANSYS14.5. The problem considered is a conjugate heat transfer from the fin, and the Navier-Stokes equation is solved to obtain the flow parameters. Grid independence study is carried out to fix the number of grids for the study considered. To reduce the computational cost, computational fluid dynamics (CFD) is replaced with artificial neural network (ANN) as the forward model. The Markov Chain Monte Carlo (MCMC) powered by Metropolis-Hastings sampling algorithm along with the Bayesian framework is used to explore the estimation space. The sensitivity analysis of the estimated temperature with respect to the unknown parameter is discussed to know the dependency of the temperature with the parameter. This paper signifies the effect of a prior model on the execution of the inverse algorithm at different noise levels. The unknown heat flux is estimated for the surrogated temperature and the estimates are reported as mean, Maximum a Posteriori (MAP) and standard deviation. The effect of a-priori information on the estimated parameter is also addressed. The standard deviation in the estimation process is referred to as the uncertainty associated with the estimated parameters.
“…They concluded that at high noise levels, Parallel Tempering (PT) and Evolutionary Monte Carlo (EMC) perform equally and estimate the parameter with the deviation of maximum 9%. Sensitivity analysis [15][16][17][18][19] is an important study that indicates the effectiveness of the change in parameter estimated on the temperature. Sensitivity analysis has control over the selection of the inverse method.…”
This paper reports the estimation of the unknown boundary heat flux from a fin using the Bayesian inference method. The setup consists of a rectangular mild steel fin of dimensions 250915096 mm 3 and an aluminium base plate of dimensions 250915098 mm 3. The fin is subjected to constant heat flux at the base and the fin setup is modelled using ANSYS14.5. The problem considered is a conjugate heat transfer from the fin, and the Navier-Stokes equation is solved to obtain the flow parameters. Grid independence study is carried out to fix the number of grids for the study considered. To reduce the computational cost, computational fluid dynamics (CFD) is replaced with artificial neural network (ANN) as the forward model. The Markov Chain Monte Carlo (MCMC) powered by Metropolis-Hastings sampling algorithm along with the Bayesian framework is used to explore the estimation space. The sensitivity analysis of the estimated temperature with respect to the unknown parameter is discussed to know the dependency of the temperature with the parameter. This paper signifies the effect of a prior model on the execution of the inverse algorithm at different noise levels. The unknown heat flux is estimated for the surrogated temperature and the estimates are reported as mean, Maximum a Posteriori (MAP) and standard deviation. The effect of a-priori information on the estimated parameter is also addressed. The standard deviation in the estimation process is referred to as the uncertainty associated with the estimated parameters.
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